Faster Chips That Don't Melt — Anima Anandkumar & Benedikt Jenik, Accelerated Understanding
Sep 4, 2026 · 27:02
Accelerated Understanding co-founders Anima Anandkumar and Benedikt Jenik argue the universality and scale of language models can extend to physics via a single foundation model trained across fluid dynamics, semiconductors, and energy. They report that models trained across multiple physics domains outperform equally sized single-domain models, evidencing genuine transfer learning. Jenik says they train at up to trillion context and infer at five trillion, with 22-terabyte outputs, requiring reinvented sharding infrastructure. Anandkumar says neural operators give resolution invariance where transformers' quadratic complexity fails. Simulator-generated curricula and physics-based self-improvement provide dense training signals, with first customers in semiconductor design and geothermal.
